Customer support agents
Assistants that resolve real tickets against your knowledge base, escalate cleanly to a human, and learn from every hand-off.
AI that does real work.
Not just demos.
We move teams from AI experiments to reliable operations. Its On Media designs agents, RAG assistants and automations around your data, approvals and existing tools — then makes them observable, secure and ready for daily use.
40–70%typical workflow
time saved
time saved
6–12 wksfrom scope to
production agent
production agent
Human + AIreview kept in the
loop by design
loop by design
Grounded in your own data (RAG)
Connected to your real tools
Guardrails & evaluation
Observable in production
Where AI pays off
Put AI where the
Put AI where the
manual work lives.
The best returns come from high-volume, repetitive work and knowledge locked across systems — not from a chatbot bolted onto a homepage.
Document automation
Extraction, summarisation and classification pipelines that turn PDFs, forms and emails into structured, actionable data.
Sales & ops automation
Agents that draft, route and update records across your CRM and tools, so your team spends time on decisions, not data entry.
Internal copilots
Private assistants that answer from your policies, code or handbooks — with permissions and an audit trail behind every answer.
AI services
Seven ways we bring
Seven ways we bring
AI into the work.
From a first integration to a fleet of agents, senior engineering stays close to the data, the approvals and the day-to-day reality of your team. Open any service to go deeper.
01
Agents that take real actions
AI agent development
Goal-driven agents that plan, call your tools and complete multi-step work — with approvals and stop conditions where they matter.
Tool & action useMulti-step planningApprovals & limitsMulti-agent flows
02
Start where your tools already are
AI integration services
Add intelligence to the products and workflows you already run — without a rebuild, and without sending your data somewhere it shouldn’t go.
OpenAI & ClaudeAPI & SDK workPrivate deploymentsPrompt engineering
03
Ground answers in your knowledge
RAG development
Retrieval pipelines that let an assistant or product feature answer from your documents and data — with citations, not confident guesses.
Vector searchKnowledge basesCitationsAccess control
04
Answers, wherever people ask
AI chatbot development
Grounded, cited assistants on your website, in Slack or over WhatsApp — that hand off cleanly to a human when a person is needed.
Web, Slack, WhatsAppGrounded answersHuman hand-offAnalytics
05
Fewer hand-offs, less busywork
AI workflow automation
End-to-end automations that move work between systems and people, replacing brittle scripts and copy-paste with something you can trust.
PipelinesSystem integrationsTriggers & queuesHuman hand-off
06
Language features in the product
LLM application development
Summarization, extraction, classification and drafting built into your software — with structured output, evaluations and reliable production engineering.
Structured outputEvaluationsModel selectionObservability
07
Know what to build, and why
AI consulting & strategy
A clear-eyed read on where AI actually pays off for you, what your data and systems are ready for, and a sequenced plan to get there.
Opportunity mappingData readinessRoadmapBuild vs buy
Built for production
A demo is easy.
A demo is easy.
Production is the job.
Most AI pilots stall on the way to daily use: no evaluation, no observability, no plan for the edge cases. We engineer for the messy reality from the first commit, so what we ship keeps working after launch.
100%runs traced & observable
Evalson every meaningful change
Your datastays under your control
Retrieval grounded in your data, with citationsGuardrails on inputs, outputs and tool useEvaluation suites that catch regressions earlyTracing and monitoring on every requestHuman review on high-stakes decisionsPrivate deployment options when data demands it
How we work
From use case
From use case
to reliable system.
A repeatable path from “could AI help here?” to something your team trusts in daily operations — measured, observable and safe to change.
Discovery & data readiness
We map the workflow, the decisions and the data behind them — then agree what “good” looks like and how we will measure it before any model is chosen.
Retrieval & knowledge
We connect your documents and systems into a retrieval layer so answers are grounded in your reality, with the right access controls in place.
Tools & actions
We give the model safe, well-scoped tools to read and act in your stack — with approvals, rate limits and clear boundaries around what it can do.
Guardrails & evaluation
We build evaluation sets and guardrails up front, so quality is a number we can watch and regressions are caught before your users find them.
Observability
Every request is traced — prompts, retrieval, tool calls and cost — so you can see what happened, debug fast and improve with evidence.
Human in the loop
For anything high-stakes, a person stays in control. We design the review points, the overrides and the audit trail so trust is earned, not assumed.
The stack we reach for
Model-agnostic by default — we choose per use case for quality, latency, privacy and cost, and keep the door open to switch.
OpenAIClaudePythonNext.jsPostgreSQL / pgvectorPrivate / on-prem
AI delivery blueprint
Turn AI from demo energy into daily work.
We define the data, tools, guardrails and human review points first, so the system is useful in production and not just impressive in a meeting.
RAGGrounded answers
APITool actions
QAHuman review
What gets clarified
AI that knows its
01
02
03
04
AI that knows its
limits and job.
Use cases worth automating
We separate useful AI workflows from novelty, then choose the highest-return place to start.
Data and retrieval design
Documents, databases and permissions are mapped so answers stay grounded in trusted sources.
Actions and approvals
Agents can draft, route and update records, while sensitive decisions stay behind human approval.
Monitoring and improvement
Logs, feedback and evaluation checks make the AI easier to improve after launch.
Direct answers
What teams ask
What teams ask
before they start.
Still weighing it up? Start with a focused technical conversation — no pitch deck required.
Ask us somethingIt depends on data readiness, integrations, security and scope. After a focused discovery we give a range tied to milestones — not an unexplained fixed number. A first production agent is commonly a 6–12 week engagement.
No. We use providers and configurations that don’t train on your data, and where privacy demands it we deploy privately or on-prem. Your data stays under your control, with access rules and logging around it.
We ground answers in your data with retrieval and citations, constrain tool use with guardrails, and measure quality with evaluation sets. For high-stakes paths a human stays in the loop, and every step is traceable.
Yes — that’s usually the point. We integrate with your CRM, databases, internal APIs and third-party services so the AI can read and act where the work already happens, within the permissions you set.
Often, yes. Most pilots stall on evaluation, observability and edge cases rather than the model. We can assess what you have, add the missing production layers and get it to something dependable — or advise honestly if a reset is the faster path.
Contact
Let’s make the next
Let’s make the next
move count.
Tell us what you are building. We will come back within one business day with questions, not a pitch deck.